Design and Implementation of a Characterization Test Rig for Evaluating High Bandwidth Liquid Fuel Flow Modulators

Saus, Joseph R.; Chang, Clarence T.; DeLaat, John C.; Vrnak, Daniel R.

doi:10.2514/6.2009-4886

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Find potential failures affecting fuel metering and supply, develop more reliable metering methods [27][28][29] Icing issue Reproduce the accident and analyse the cause to improve the existing system [30,31] Fuel filter Explore and resolve potential failures and challenges presented by new technologies/fuels [32][33][34][35][36][37] Other vulnerable components…”

Section: Referencesmentioning

confidence: 99%

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Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

King

Jennions

2023

Machines

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The fuel system, which aims to provide sufficient fuel to the engine to maintain thrust and power, is one of the most critical systems in the aircraft. However, possible degradation modes, such as leakage and blockage, can lead to component failure, affect performance, and even cause serious accidents. As an advanced maintenance strategy, Condition Based Maintenance (CBM) can provide effective coverage, by combining state-of-the-art sensors with data acquisition and analysis techniques to guide maintenance before the asset’s degradation becomes serious. Artificial Intelligence (AI), particularly machine learning (ML), has proved effective in supporting CBM, for analyzing data and generating predictions regarding the asset’s health condition, thus influencing maintenance plans. However, from an engineering perspective, the output of ML algorithms, usually in the form of data-driven neural networks, has come into question in practice, as it can be non-intuitive and lacks the ability to provide unambiguous engineering signals to maintainers, making it difficult to trust. Engineers are interested in a deterministic decision-making process and how it is being revealed; algorithms should be able to certify and convince engineers to approve recommended actions. Explainable AI (XAI) has emerged as a potential solution, providing some of the logic on how the output is derived from the input given, which may help users understand the diagnostic result of the algorithm. In order to inspire and advise data scientists and engineers who are about to develop and use AI approaches in fuel systems, this paper explores the literature of experiment, simulation, and AI-based diagnostics for the fuel system to make an informed statement as to the progress that has been made in intelligent fault diagnostics for fuel systems, emphasizing the necessity of giving unambiguous engineering signals to maintainers, as well as highlighting potential areas for future research.

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Section: Referencesmentioning

confidence: 99%

“…Other topics associated with fuel systems have been reported in the literature: flow modulators [32], refuelling [33], and electrostatic charge [34]. Furthermore, accurate fuel flow measurement is always a problem since the flowmeter needs to be placed directly in the flow field.…”

Section: Referencesmentioning

confidence: 99%

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

King

Jennions

2023

Machines

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show abstract

“…Using a dynamic flow test rig (Ref. 24), the fuel valve was dynamically characterized to try to predict how well it would be able to modulate the pilot fuel. Fuel line lengths and sizes were set in the characterization rig to approximate the lengths and volumes for the fuel feed system in the combustor rig.…”

Section: High-frequency Fuel Actuatormentioning

confidence: 99%

Active Combustion Control for Aircraft Gas-Turbine Engines - Experimental Results for an Advanced, Low-Emissions Combustor Prototype

DeLaat

Kopasakis

Saus

et al. 2012

50th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition

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Active Combustion Control for a Low-Emissions Aircraft Engine Combustor Prototype: Experimental Results

DeLaat

Kopasakis

Saus

et al. 2013

Journal of Propulsion and Power

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Design and Implementation of a Characterization Test Rig for Evaluating High Bandwidth Liquid Fuel Flow Modulators

Cited by 3 publications

References 6 publications

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

Intelligent Fault Diagnosis of an Aircraft Fuel System Using Machine Learning—A Literature Review

Active Combustion Control for Aircraft Gas-Turbine Engines - Experimental Results for an Advanced, Low-Emissions Combustor Prototype

Active Combustion Control for a Low-Emissions Aircraft Engine Combustor Prototype: Experimental Results

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